Face Generation¶
In this project, you'll use generative adversarial networks to generate new images of faces.
Get the Data¶
You'll be using two datasets in this project:
- MNIST
- CelebA
Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.
If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".
data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'
"""
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"""
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
show_n_images = 25
"""
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"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
CelebA¶
The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.
show_n_images = 25
"""
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"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Preprocess the Data¶
Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.
The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).
Build the Neural Network¶
You'll build the components necessary to build a GANs by implementing the following functions below:
model_inputsdiscriminatorgeneratormodel_lossmodel_opttrain
Check the Version of TensorFlow and Access to GPU¶
This will check to make sure you have the correct version of TensorFlow and access to a GPU
"""
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"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer. You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
Input¶
Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:
- Real input images placeholder with rank 4 using
image_width,image_height, andimage_channels. - Z input placeholder with rank 2 using
z_dim. - Learning rate placeholder with rank 0.
Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)
import problem_unittests as tests
def model_inputs(image_width, image_height, image_channels, z_dim):
"""
Create the model inputs
:param image_width: The input image width
:param image_height: The input image height
:param image_channels: The number of image channels
:param z_dim: The dimension of Z
:return: Tuple of (tensor of real input images, tensor of z data, learning rate)
"""
inputs_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='inputs_real')
inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='inputs_z')
learning_rate = tf.placeholder(tf.float32)
return inputs_real, inputs_z, learning_rate
"""
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"""
tests.test_model_inputs(model_inputs)
Discriminator¶
Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).
def leaky_relu(tensor, alpha=0.1):
return tf.maximum(tensor * alpha, tensor)
def discriminator(images, reuse=False):
"""
Create the discriminator network
:param image: Tensor of input image(s)
:param reuse: Boolean if the weights should be reused
:return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
"""
with tf.variable_scope('discriminator', reuse=reuse):
# Use a stride of two to reduce size.
# Nothing special otherwise.
conv1 = tf.layers.conv2d(images, 32, 3, 1, padding='same', activation=None, kernel_initializer=tf.contrib.layers.xavier_initializer())
conv1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[3, 3], strides=2)
conv1 = tf.layers.batch_normalization(conv1, training=True)
conv1 = leaky_relu(conv1)
#conv1 = tf.layers.dropout(conv1, 0.5)
# Just another convolution layer. Increasing the filter count.
conv2 = tf.layers.conv2d(conv1, 64, 3, 1, padding='same', activation=None, kernel_initializer=tf.contrib.layers.xavier_initializer())
conv2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[3, 3], strides=2)
conv2 = tf.layers.batch_normalization(conv2, training=True)
conv2 = leaky_relu(conv2)
#conv2 = tf.layers.dropout(conv2, 0.5)
# A convolution layer without downsizing
conv3 = tf.layers.conv2d(conv2, 64, 7, padding='same', activation=None, kernel_initializer=tf.contrib.layers.xavier_initializer())
conv3 = tf.layers.batch_normalization(conv3, training=True)
conv3 = leaky_relu(conv3)
#conv3 = tf.layers.dropout(conv3, 0.5)
#and another
conv4 = tf.layers.conv2d(conv3, 64, 3, padding='same', activation=None, kernel_initializer=tf.contrib.layers.xavier_initializer())
conv4 = tf.layers.batch_normalization(conv4, training=True)
conv4 = leaky_relu(conv4)
conv4 = tf.layers.dropout(conv4, 0.2)
flattened = tf.contrib.layers.flatten(conv4)
logits = tf.layers.dense(flattened, 1, activation=None, kernel_initializer=tf.contrib.layers.xavier_initializer())
out = tf.sigmoid(logits)
return out, logits
"""
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"""
tests.test_discriminator(discriminator, tf)
Generator¶
Implement generator to generate an image using z. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.
# adds batch normalization and leaky relu to a layer
def batch_normalize_and_activate(inputs, is_train):
normalized = tf.layers.batch_normalization(inputs, training=is_train)
with_activation = leaky_relu(normalized)
return with_activation
def upsample_and_convolve(inputs, neighborhood_shape, filter_count, filter_shape, padding, is_train, normalize=True):
resized = tf.image.resize_nearest_neighbor(inputs, neighborhood_shape)
convolved = tf.layers.conv2d(resized, filter_count, filter_shape, padding=padding, activation=None)
if normalize:
convolved = batch_normalize_and_activate(convolved, is_train)
else:
convolved = leaky_relu(convolved)
return convolved
def transpose_convolve(inputs, filter_count, is_train, kernel_size=4, strides=2, padding='same', normalize=True):
convolved = tf.layers.conv2d_transpose(inputs, filter_count, kernel_size, strides=strides, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
if normalize:
convolved = batch_normalize_and_activate(convolved, is_train)
return convolved
def generator(z, out_channel_dim, is_train=True):
"""
Create the generator network
:param z: Input z
:param out_channel_dim: The number of channels in the output image
:param is_train: Boolean if generator is being used for training
:return: The tensor output of the generator
"""
with tf.variable_scope('generator', reuse = (not is_train)):
# add a fully-connected layer
hidden_layer_implicit_width = 12
hidden_layer_size = 16 * hidden_layer_implicit_width * hidden_layer_implicit_width
h1 = tf.layers.dense(z, hidden_layer_size, kernel_initializer=tf.contrib.layers.xavier_initializer())
h1 = batch_normalize_and_activate(h1, is_train)
# high dropout with this early wide layer prevents overfitting
h1 = tf.layers.dropout(h1, 0.7, training=is_train)
reshaped = tf.reshape(h1, [-1, hidden_layer_implicit_width, hidden_layer_implicit_width, hidden_layer_size // (hidden_layer_implicit_width * hidden_layer_implicit_width)])
up1 = upsample_and_convolve(reshaped, (14, 14), 256, (3,3), 'same', is_train)
up1 = tf.layers.dropout(up1, 0.5, training=is_train)
up2 = upsample_and_convolve(up1, (16, 16), 256, (3,3), 'same', is_train)
up2 = tf.layers.dropout(up2, 0.5, training=is_train)
up3 = upsample_and_convolve(up2, (18, 18), 256, (3,3), 'same', is_train)
up3 = tf.layers.dropout(up3, 0.5, training=is_train)
up4 = upsample_and_convolve(up3, (20, 20), 256, (3,3), 'same', is_train)
up4 = tf.layers.dropout(up4, 0.5, training=is_train)
up5 = upsample_and_convolve(up4, (22, 22), 256, (7,7), 'same', is_train)
up5 = tf.layers.dropout(up5, 0.5, training=is_train)
up6 = upsample_and_convolve(up5, (24, 24), 256, (5,5), 'same', is_train)
up6 = tf.layers.dropout(up6, 0.5, training=is_train)
# I have found that removing batch normalization in late layers
# helps prevent weird artifacts
up7 = upsample_and_convolve(up6, (28, 28), 256, (4,4), 'same', is_train, normalize=False)
up7 = tf.layers.dropout(up7, 0.5, training=is_train)
# run a same-size convolution on the resulting output to sharpen it up
same = tf.layers.conv2d(up7, 128, 4, padding='same', activation=None, kernel_initializer=tf.contrib.layers.xavier_initializer())
#same = batch_normalize_and_activate(same, is_train)
same = leaky_relu(same)
same = tf.layers.dropout(same, 0.5, training=is_train)
#and another one
same = tf.layers.conv2d(same, 128, 2, padding='same', activation=None, kernel_initializer=tf.contrib.layers.xavier_initializer())
#same = batch_normalize_and_activate(same, is_train)
same = leaky_relu(same)
same = tf.layers.dropout(same, 0.3, training=is_train)
#and another
same = tf.layers.conv2d(same, 128, 1, padding='same', activation=None, kernel_initializer=tf.contrib.layers.xavier_initializer())
#same = batch_normalize_and_activate(same, is_train)
same = leaky_relu(same)
same = tf.layers.dropout(same, 0.1, training=is_train)
# drop to the appropriate number of channels with a final convolution
logits = tf.layers.conv2d(same, out_channel_dim, 1, padding='same', activation=None, kernel_initializer=tf.contrib.layers.xavier_initializer())
out = tf.tanh(logits)
return out
"""
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"""
tests.test_generator(generator, tf)
Loss¶
Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:
discriminator(images, reuse=False)generator(z, out_channel_dim, is_train=True)
def model_loss(input_real, input_z, out_channel_dim):
"""
Get the loss for the discriminator and generator
:param input_real: Images from the real dataset
:param input_z: Z input
:param out_channel_dim: The number of channels in the output image
:return: A tuple of (discriminator loss, generator loss)
"""
smooth = 0.1
g_model = generator(input_z, out_channel_dim, is_train=True)
d_model_real, d_logits_real = discriminator(input_real, reuse=False)
d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real,
labels=tf.ones_like(d_logits_real) * (1 - smooth)))
d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,
labels=tf.zeros_like(d_logits_fake)))
d_loss = d_loss_real + d_loss_fake
g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,
labels=tf.ones_like(d_logits_fake)))
return d_loss, g_loss
"""
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"""
tests.test_model_loss(model_loss)
Optimization¶
Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).
def model_opt(d_loss, g_loss, learning_rate, beta1):
"""
Get optimization operations
:param d_loss: Discriminator loss Tensor
:param g_loss: Generator loss Tensor
:param learning_rate: Learning Rate Placeholder
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:return: A tuple of (discriminator training operation, generator training operation)
"""
# Get the trainable_variables, split into G and D parts
t_vars = tf.trainable_variables()
g_vars = [variable for variable in t_vars if variable.name.startswith('generator')]
d_vars = [variable for variable in t_vars if variable.name.startswith('discriminator')]
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
return d_train_opt, g_train_opt
"""
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"""
tests.test_model_opt(model_opt, tf)
"""
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"""
import numpy as np
def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
"""
Show example output for the generator
:param sess: TensorFlow session
:param n_images: Number of Images to display
:param input_z: Input Z Tensor
:param out_channel_dim: The number of channels in the output image
:param image_mode: The mode to use for images ("RGB" or "L")
"""
cmap = None if image_mode == 'RGB' else 'gray'
z_dim = input_z.get_shape().as_list()[-1]
example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])
samples = sess.run(
generator(input_z, out_channel_dim, False),
feed_dict={input_z: example_z})
images_grid = helper.images_square_grid(samples, image_mode)
pyplot.imshow(images_grid, cmap=cmap)
pyplot.show()
Train¶
Implement train to build and train the GANs. Use the following functions you implemented:
model_inputs(image_width, image_height, image_channels, z_dim)model_loss(input_real, input_z, out_channel_dim)model_opt(d_loss, g_loss, learning_rate, beta1)
Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
"""
Train the GAN
:param epoch_count: Number of epochs
:param batch_size: Batch Size
:param z_dim: Z dimension
:param learning_rate: Learning Rate
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:param get_batches: Function to get batches
:param data_shape: Shape of the data
:param data_image_mode: The image mode to use for images ("RGB" or "L")
"""
# model definition
_, image_width, image_height, image_channels = list(data_shape)
input_real, input_z, _ = model_inputs(image_width, image_height, image_channels, z_dim)
d_loss, g_loss = model_loss(input_real, input_z, image_channels)
d_train_opt, g_train_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
# training
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch_i in range(epoch_count):
batch_number = 0
for batch_images in get_batches(batch_size):
batch_number += 1
#scale properly
batch_images = batch_images * 2
# Sample random noise for G
batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
# Run optimizers
_ = sess.run(d_train_opt, feed_dict={input_real: batch_images, input_z: batch_z})
# the generator needs more training than the discriminator.
# let it get there.
train_iterations = 0
train_loss_d = sess.run(d_loss, {input_z: batch_z, input_real: batch_images})
while g_loss.eval({input_z: batch_z, input_real: batch_images}) > train_loss_d and train_iterations < 5:
_ = sess.run(g_train_opt, feed_dict={input_z: batch_z, input_real: batch_images})
train_iterations += 1
# display sample images every 100 batches
if batch_number % 100 == 0:
show_generator_output(sess, 25, input_z, image_channels, data_image_mode)
# get the losses and print them out
train_loss_d = sess.run(d_loss, {input_z: batch_z, input_real: batch_images})
train_loss_g = g_loss.eval({input_z: batch_z, input_real: batch_images})
print("Epoch {}/{} |".format(epoch_i + 1, epoch_count),
"Batch number {} |".format(batch_number),
"Discriminator Loss: {:.4f} |".format(train_loss_d),
"Generator Loss: {:.4f}".format(train_loss_g))
MNIST¶
Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.
batch_size = 32
z_dim = 128
learning_rate = 0.0001
beta1 = 0.5
"""
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"""
epochs = 2
# might need to run this more than once :)
tf.reset_default_graph()
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
mnist_dataset.shape, mnist_dataset.image_mode)
CelebA¶
Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.
batch_size = 32
z_dim = 128
learning_rate = 0.0001
beta1 = 0.5
"""
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"""
epochs = 1
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
celeba_dataset.shape, celeba_dataset.image_mode)
Submitting This Project¶
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.